Dopamine improves defective cortical and muscular connectivity during bilateral control of gait in Parkinson’s disease

Parkinson’s Disease (PD)-typical declines in gait coordination are possibly explained by weakness in bilateral cortical and muscular connectivity. Here, we seek to determine whether this weakness and consequent decline in gait coordination is affected by dopamine levels. To this end, we compare cortico-cortical, cortico-muscular, and intermuscular connectivity and gait outcomes between body sides in people with PD under ON and OFF medication states, and in older adults. In our study, participants walked back and forth along a 12 m corridor. Gait events (heel strikes and toe-offs) and electrical cortical and muscular activities were measured and used to compute cortico-cortical, cortico-muscular, and intermuscular connectivity (i.e., coherences in the alpha, beta, and gamma bands), as well as features characterizing gait performance (e.g., the step-timing coordination, length, and speed). We observe that people with PD, mainly during the OFF medication, walk with reduced step-timing coordination. Additionally, our results suggest that dopamine intake in PD increases the overall cortico-muscular connectivity during the stance and swing phases of gait. We thus conclude that dopamine corrects defective feedback caused by impaired sensory-information processing and sensory-motor integration, thus increasing cortico-muscular coherences in the alpha bands and improving gait.

We thank the reviewers for their time and insightful comments on improving the manuscript.We have addressed/answered each criticism and indicated the changes we made in the manuscript (blue).

Reviewers' comments:
Reviewer #1 (Remarks to the Author): This is a revised version of a manuscript that I previously reviewed.The authors have addressed several issues raised by the previous reviewers.However, the revision seems somewhat superficial.Specifically, in the opinion of this reviewer, the authors have not sufficiently focused the paper or improved its readability.This primarily concerns the presentation of data, but also encompasses language-related issues.While the data may hold value for a specialist audience, the overall value of the present manuscript seems limited for a broader readership.RESPONSE: Thank you for your comments, which certainly helped improve the quality of the manuscript.We sent the manuscript for language editorial service, and we believe that this process helped improve the presentation of the data and language-related issues.In the tracked version, we highlighted in blue some of the changes provided by the service.We also selected to report corticocortical, cortico-muscular, and intermuscular coherence in the alpha, beta, or gamma band instead of cortico-cortical, cortico-muscular, and intermuscular alpha, beta, or gamma coherence.We edited the figures by increasing their font sizes and their qualities to improve the presentation of the data.We also changed segments that the language service identified as unclear or complex to follow (highlighted in blue in the manuscript track version).
Regarding specific concerns raised previously, the authors have misconstrued my argument.Coherence analyses are categorized into specific phases of gait (stance, swing), each of which is defined kinematically.Since corticomuscular coherence pertains to muscular activity, it might be more enlightening to utilize muscular activity to distinguish between different phases.This contention is distinct from the matter of how the analysis of muscle synergies might enhance our comprehension of freezing of gait, as now introduced by the authors.RESPONSE: First, we would like to acknowledge that we did not respond properly to this comment during the previous revision round.We focused on potentially addressing 'muscle synergies' rather than on the method of timing the gait phases (i.e., EMG vs 'kinematics').We thank you for this interesting suggestion for window coherence analysis to EMG burst instead of kinematics.However, there are several challenges and limitations in determining gait phases based on EMG, as follows: 1) Electrophysiological data, in general, and EMG, in particular, have a considerably variable pattern during cyclic movement within and between individuals and muscles.Thus, participants from the different groups (PD and Older) and medication conditions (ON vs. OFF) may have even greater variability in patterns (i.e., when in the cycle the muscles are active), peaks (amplitude), and in the temporal features of the EMG burst during walking that is not time-locked.Thus, EMG-based windowing (segmentation) of the data would lead to misleading results, conclusions.The reason is that the basis of computation of coherence would differ between participants per se AND in relation to a determined cycle.Potentially, we would include data from one participant/cycle that would cancel data from another participant/cycle.Therefore, we particularly do not know whether to account for this variability to create a robust method that allows us to predict and identify events during gait based on EMG signals, and we are not familiar with any study that proposed to compute coherence during gait based on the EMG window (listed below).
2) Differently, kinematics, specifically considering OPAL [ (Mancini et al. 2011;Spain et al. 2012;Morris et al. 2019) see other at https://apdm.com/publications/],and kinetics (e.g., force plate data) is a more robust and validated method to determine gait events and metrics.With such methods, we can also rely on specific events to occur independent of the individual variability in muscle activation without a greater variation in time.Therefore agreeing with an extensive number of studies ( (Halliday et al. 2003;Norton and Gorassini 2006;Nielsen et al. 2008;Petersen et al. 2012;Artoni et al. 2017;Roeder et al. 2018Roeder et al. , 2020;;Spedden et al. 2019;Jensen et al. 2019;Yokoyama et al. 2020;Gennaro and Bruin 2020;Santos et al. 2020Santos et al. , 2022;;Weersink et al. 2021b, a;Sato and Choi 2022;Feng et al. 2023), we argue that computing coherence analysis by windowing the data relative to kinematically and kinetically determined gait cycle instead of EMG burst onset/offset represents a more functional way and facilitate the interpretation and comparison with the literature.3) Finally, although reconstructed data based on principal components from health subjects theoretically suggest a relative pattern in muscle activation (e.g., gastrocnemius activate in specific periods of gait cycle -near to the toe-off), the selection would be arbitrary considering that the muscles analyzed are predominantly active at different times during the gait cycle (e.g., Tibial is active around the heel strike, and Rectus femoris activates mainly after the heel strike).Therefore, considering classifying the cycle based on one muscle does not necessarily ensure the activation of another muscle at the same window.This would not only interfere in determining cycles and the functional interpretation (or clinical meaning) but also in selecting the window for computing intermuscular coherence.
We however recognize that it might be relevant to future studies to develop robust methodologies in a way that allows us to predict and determine windows of analysis based on EMG burst (onset and offset) for computing spectral analysis such as coherence.We included the following statement in the limitation of the study: "Another relevant direction for future studies is the development of windows for spectral analysis (e.g., such as coherence) based on EMG signals instead of kinematics or kinetics.This would increase the "ecological" validity of the analysis, as the windows would potentially be more representative in terms of analyzing the window in which a certain muscle is active.However, implementing such methods during gait analysis poses challenges due to the diverse muscular functions, activity patterns, and peaks of muscles, as well as the inherent variability in electrophysiological data across individuals and within steps." As episodes of FOG were excluded from the analysis, any finding of corticomuscular coherence deviations can only very indirectly be related to the pathophysiology of FOG.RESPONSE: Thank you for your suggestion.We completely agree with the reviewer.We did have enough FOG events for performing a robust coherence analysis (the resolution/validity of the analysis increases according to the increase in the number of segments (steps/events) analyzed (Halliday et al. 1995)), and thus, we decided to exclude the events.Additionally, we agree that we can only indirectly relate the analysis with the pathophysiology of FOG.We reviewed the entire manuscript to ensure that we can only indirectly relate this to FOG, and we included a new segment in the future direction paragraph.
"Also, a relevant question for future studies would be to verify the link between cortico-cortical, cortico-muscular, and intermuscular coherence with the pathophysiology of FoG since we could only verify the indirect association between coherence and FoG, as we did not have enough events to compute a robust coherence analysis."